In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. However, their widespread application in mental state decoding is hindered by their lack of interpretability, difficulties in applying them to small datasets, and in ensuring their reproducibility and robustness. We recommend approaching these challenges by leveraging recent advances in explainable artificial intelligence (XAI) and transfer learning, and also provide recommendations on how to improve the reproducibility and robustness of DL models in mental state decoding.

在心理状态解码中，研究人员的目标是确定可以从大脑区域（或网络）的活动模式中可靠识别的一组心理状态（例如，体验快乐或恐惧）。深度学习 (DL) 模型非常有希望用于心理状态解码，因为它们具有无与伦比的学习复杂数据通用表示的能力。然而，由于缺乏可解释性、难以将其应用于小型数据集以及确保其可重复性和稳健性，它们在心理状态解码中的广泛应用受到阻碍。我们建议利用可解释人工智能 (XAI) 和迁移学习的最新进展来应对这些挑战，并就如何提高 DL 模型在心理状态解码中的可重复性和鲁棒性提出建议。